{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 逻辑回归（Logistic Regression)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1596912f4a9c422b"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-23T03:19:44.061741Z",
     "start_time": "2025-05-23T03:19:44.057128Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import time\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 加载数据及数据预处理"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "737764cbc9b4bfc9"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据集路径\n",
    "data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'\n",
    "# 加载CIFAR-10数据集\n",
    "def load_cifar10_data(data_dir):\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_path = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        with open(batch_path, 'rb') as file:\n",
    "            batch = pickle.load(file, encoding='latin1')\n",
    "            train_data.append(batch['data'])\n",
    "            train_labels.extend(batch['labels'])\n",
    "    train_data = np.array(train_data).reshape(50000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_batch_path = os.path.join(data_dir, 'test_batch')\n",
    "    with open(test_batch_path, 'rb') as file:\n",
    "        test_batch = pickle.load(file, encoding='latin1')\n",
    "    test_data = test_batch['data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    test_labels = np.array(test_batch['labels'])\n",
    "\n",
    "    return (train_data, train_labels), (test_data, test_labels)\n",
    "\n",
    "# 调用函数加载数据\n",
    "(train_data, train_labels), (test_data, test_labels) = load_cifar10_data(data_dir)\n",
    "\n",
    "# 数据预处理\n",
    "train_data = train_data.astype('float32') / 255.0\n",
    "test_data = test_data.astype('float32') / 255.0\n",
    "\n",
    "# 将图像数据展平为一维向量\n",
    "train_data = train_data.reshape(-1, 32*32*3)\n",
    "test_data = test_data.reshape(-1, 32*32*3)\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "train_data = scaler.fit_transform(train_data)\n",
    "test_data = scaler.transform(test_data)\n",
    "\n",
    "# 使用PCA降维\n",
    "pca = PCA(n_components=0.95)  # 保留95%的方差\n",
    "train_data = pca.fit_transform(train_data)\n",
    "test_data = pca.transform(test_data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-23T03:35:17.762397Z",
     "start_time": "2025-05-23T03:19:46.888961Z"
    }
   },
   "id": "c0ca0c13af72d19d",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 创建模型及模型训练评估"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "46e9a07d25fc6d49"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python data analysis\\anaconda\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练时间: 9.54秒\n",
      "Accuracy: 0.4058\n",
      "Precision: 0.3992537681727604\n",
      "Recall: 0.4058\n",
      "F1-score: 0.40027744841888113\n",
      "Confusion Matrix:\n",
      " [[498  52  39  27  22  26  25  52 183  76]\n",
      " [ 65 484  21  32  26  33  43  59  71 166]\n",
      " [ 99  49 252  90 114  79 158  81  53  25]\n",
      " [ 51  60  91 250  56 177 141  55  50  69]\n",
      " [ 61  31 139  59 283  89 168 118  20  32]\n",
      " [ 45  46 103 150  65 340  97  77  51  26]\n",
      " [ 18  38  65 112  84  73 522  37  23  28]\n",
      " [ 52  51  67  62  88  77  50 431  44  78]\n",
      " [161  69  13  26  10  45  15  23 534 104]\n",
      " [ 79 177  19  21  14  21  50  61  94 464]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.50      0.47      1000\n",
      "           1       0.46      0.48      0.47      1000\n",
      "           2       0.31      0.25      0.28      1000\n",
      "           3       0.30      0.25      0.27      1000\n",
      "           4       0.37      0.28      0.32      1000\n",
      "           5       0.35      0.34      0.35      1000\n",
      "           6       0.41      0.52      0.46      1000\n",
      "           7       0.43      0.43      0.43      1000\n",
      "           8       0.48      0.53      0.50      1000\n",
      "           9       0.43      0.46      0.45      1000\n",
      "\n",
      "    accuracy                           0.41     10000\n",
      "   macro avg       0.40      0.41      0.40     10000\n",
      "weighted avg       0.40      0.41      0.40     10000\n"
     ]
    }
   ],
   "source": [
    "# 创建逻辑回归模型\n",
    "clf = LogisticRegression(max_iter=100, solver='lbfgs', multi_class='ovr')\n",
    "# 模型训练\n",
    "start_time = time.time()\n",
    "clf.fit(train_data, train_labels)\n",
    "train_time = time.time() - start_time\n",
    "print(f\"训练时间: {train_time:.2f}秒\")\n",
    "# 模型预测\n",
    "y_pred = clf.predict(test_data)\n",
    "# 模型评估\n",
    "accuracy = accuracy_score(test_labels, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(test_labels, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(test_labels, y_pred)\n",
    "class_report = classification_report(test_labels, y_pred)\n",
    "# 输出结果\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-23T03:39:57.565697Z",
     "start_time": "2025-05-23T03:39:47.985977Z"
    }
   },
   "id": "fea4455f5bc9d417",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 使用热力图绘制混淆矩阵"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e9ec902a70a786a2"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Logistic Regression - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-23T03:40:04.273230Z",
     "start_time": "2025-05-23T03:40:03.692241Z"
    }
   },
   "id": "70c73ff832835480",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "eed28a2a44d307a3"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
